{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.制表，将数据导入pandas中，加上列名"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>api</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>interval</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>46828</th>\n",
       "      <td>3955692</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>8</td>\n",
       "      <td>1365.88</td>\n",
       "      <td>129.74</td>\n",
       "      <td>257.64</td>\n",
       "      <td>170.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-12-25 16:56:46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135712</th>\n",
       "      <td>10065019</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>4</td>\n",
       "      <td>609.50</td>\n",
       "      <td>72.28</td>\n",
       "      <td>294.17</td>\n",
       "      <td>152.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-04-11 12:56:27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114569</th>\n",
       "      <td>8477208</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>17</td>\n",
       "      <td>4292.82</td>\n",
       "      <td>83.02</td>\n",
       "      <td>1088.20</td>\n",
       "      <td>252.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-17 21:58:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175143</th>\n",
       "      <td>13097544</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>3</td>\n",
       "      <td>457.10</td>\n",
       "      <td>72.72</td>\n",
       "      <td>215.57</td>\n",
       "      <td>152.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-26 01:02:16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142649</th>\n",
       "      <td>10594730</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>4</td>\n",
       "      <td>937.57</td>\n",
       "      <td>167.85</td>\n",
       "      <td>338.02</td>\n",
       "      <td>234.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-04-19 11:12:36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14213</th>\n",
       "      <td>1431129</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>8</td>\n",
       "      <td>1569.57</td>\n",
       "      <td>87.85</td>\n",
       "      <td>337.33</td>\n",
       "      <td>196.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-17 16:28:39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              id                     api  count  res_time_sum  res_time_min  \\\n",
       "46828    3955692  /front-api/bill/create      8       1365.88        129.74   \n",
       "135712  10065019  /front-api/bill/create      4        609.50         72.28   \n",
       "114569   8477208  /front-api/bill/create     17       4292.82         83.02   \n",
       "175143  13097544  /front-api/bill/create      3        457.10         72.72   \n",
       "142649  10594730  /front-api/bill/create      4        937.57        167.85   \n",
       "14213    1431129  /front-api/bill/create      8       1569.57         87.85   \n",
       "\n",
       "        res_time_max  res_time_avg  interval           created_at  \n",
       "46828         257.64         170.0        60  2018-12-25 16:56:46  \n",
       "135712        294.17         152.0        60  2019-04-11 12:56:27  \n",
       "114569       1088.20         252.0        60  2019-03-17 21:58:02  \n",
       "175143        215.57         152.0        60  2019-05-26 01:02:16  \n",
       "142649        338.02         234.0        60  2019-04-19 11:12:36  \n",
       "14213         337.33         196.0        60  2018-11-17 16:28:39  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_table('./log.txt',header=None,names=['id','api','count','res_time_sum','res_time_min','res_time_max','res_time_avg','interval','created_at'])\n",
    "df.sample(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.检测重复值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 179496 entries, 0 to 179495\n",
      "Data columns (total 9 columns):\n",
      " #   Column        Non-Null Count   Dtype  \n",
      "---  ------        --------------   -----  \n",
      " 0   id            179496 non-null  int64  \n",
      " 1   api           179496 non-null  object \n",
      " 2   count         179496 non-null  int64  \n",
      " 3   res_time_sum  179496 non-null  float64\n",
      " 4   res_time_min  179496 non-null  float64\n",
      " 5   res_time_max  179496 non-null  float64\n",
      " 6   res_time_avg  179496 non-null  float64\n",
      " 7   interval      179496 non-null  int64  \n",
      " 8   created_at    179496 non-null  object \n",
      "dtypes: float64(4), int64(3), object(2)\n",
      "memory usage: 12.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                     179496\n",
       "unique                         1\n",
       "top       /front-api/bill/create\n",
       "freq                      179496\n",
       "Name: api, dtype: object"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['api'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['/front-api/bill/create'], dtype=object)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.api.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# api为object对象 且全部重复"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    179496.0\n",
       "mean         60.0\n",
       "std           0.0\n",
       "min          60.0\n",
       "25%          60.0\n",
       "50%          60.0\n",
       "75%          60.0\n",
       "max          60.0\n",
       "Name: interval, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['interval'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([60])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.interval.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# interval为浮点数 最大值最小值平均值均为60 标准差为0 故全部重复"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4.api和interval这两列的数据均为重复的列 对分析无用 为减少占地存储空间 故删除整列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>created_at</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019162542</td>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162644</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162742</td>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>162808</td>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>162943</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id  count  res_time_sum  res_time_min  res_time_max  res_time_avg  \\\n",
       "0  2019162542      8       1057.31         88.75        177.72         132.0   \n",
       "1      162644      5        749.12        103.79        240.38         149.0   \n",
       "2      162742      5        845.84        136.31        225.73         169.0   \n",
       "3      162808      9       1305.52         90.12        196.61         145.0   \n",
       "4      162943      3        568.89        138.45        232.02         189.0   \n",
       "\n",
       "            created_at  \n",
       "0  2018-11-01 00:00:07  \n",
       "1  2018-11-01 00:01:07  \n",
       "2  2018-11-01 00:02:07  \n",
       "3  2018-11-01 00:03:07  \n",
       "4  2018-11-01 00:04:07  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=df.drop(['api','interval'],axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 179496 entries, 0 to 179495\n",
      "Data columns (total 7 columns):\n",
      " #   Column        Non-Null Count   Dtype  \n",
      "---  ------        --------------   -----  \n",
      " 0   id            179496 non-null  int64  \n",
      " 1   count         179496 non-null  int64  \n",
      " 2   res_time_sum  179496 non-null  float64\n",
      " 3   res_time_min  179496 non-null  float64\n",
      " 4   res_time_max  179496 non-null  float64\n",
      " 5   res_time_avg  179496 non-null  float64\n",
      " 6   created_at    179496 non-null  object \n",
      "dtypes: float64(4), int64(2), object(1)\n",
      "memory usage: 9.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info() #删除api和interval两列数据后 内存从12.3MB减少为9.6MB"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5.使用created_at这一列的数据作为时间索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=179496, step=1)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index #查看原先的索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:00:07</th>\n",
       "      <td>2019162542</td>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
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       "    <tr>\n",
       "      <th>2018-11-01 00:01:07</th>\n",
       "      <td>162644</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
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       "    <tr>\n",
       "      <th>2018-11-01 00:02:07</th>\n",
       "      <td>162742</td>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:03:07</th>\n",
       "      <td>162808</td>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:04:07</th>\n",
       "      <td>162943</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             id  count  res_time_sum  res_time_min  \\\n",
       "created_at                                                           \n",
       "2018-11-01 00:00:07  2019162542      8       1057.31         88.75   \n",
       "2018-11-01 00:01:07      162644      5        749.12        103.79   \n",
       "2018-11-01 00:02:07      162742      5        845.84        136.31   \n",
       "2018-11-01 00:03:07      162808      9       1305.52         90.12   \n",
       "2018-11-01 00:04:07      162943      3        568.89        138.45   \n",
       "\n",
       "                     res_time_max  res_time_avg           created_at  \n",
       "created_at                                                            \n",
       "2018-11-01 00:00:07        177.72         132.0  2018-11-01 00:00:07  \n",
       "2018-11-01 00:01:07        240.38         149.0  2018-11-01 00:01:07  \n",
       "2018-11-01 00:02:07        225.73         169.0  2018-11-01 00:02:07  \n",
       "2018-11-01 00:03:07        196.61         145.0  2018-11-01 00:03:07  \n",
       "2018-11-01 00:04:07        232.02         189.0  2018-11-01 00:04:07  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index=df['created_at'] #将created_at作为新的索引\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['2018-11-01 00:00:07', '2018-11-01 00:01:07', '2018-11-01 00:02:07',\n",
       "       '2018-11-01 00:03:07', '2018-11-01 00:04:07', '2018-11-01 00:05:07',\n",
       "       '2018-11-01 00:06:07', '2018-11-01 00:07:07', '2018-11-01 00:08:07',\n",
       "       '2018-11-01 00:09:07',\n",
       "       ...\n",
       "       '2019-05-30 23:01:21', '2019-05-30 23:02:21', '2019-05-30 23:03:21',\n",
       "       '2019-05-30 23:04:21', '2019-05-30 23:05:21', '2019-05-30 23:06:21',\n",
       "       '2019-05-30 23:07:21', '2019-05-30 23:08:21', '2019-05-30 23:09:21',\n",
       "       '2019-05-30 23:10:21'],\n",
       "      dtype='object', name='created_at', length=179496)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index  #created_at时间索引为object（字符串）对象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index=pd.to_datetime(df.created_at)  #将时间索引改为datetime对象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-11-01 00:00:07', '2018-11-01 00:01:07',\n",
       "               '2018-11-01 00:02:07', '2018-11-01 00:03:07',\n",
       "               '2018-11-01 00:04:07', '2018-11-01 00:05:07',\n",
       "               '2018-11-01 00:06:07', '2018-11-01 00:07:07',\n",
       "               '2018-11-01 00:08:07', '2018-11-01 00:09:07',\n",
       "               ...\n",
       "               '2019-05-30 23:01:21', '2019-05-30 23:02:21',\n",
       "               '2019-05-30 23:03:21', '2019-05-30 23:04:21',\n",
       "               '2019-05-30 23:05:21', '2019-05-30 23:06:21',\n",
       "               '2019-05-30 23:07:21', '2019-05-30 23:08:21',\n",
       "               '2019-05-30 23:09:21', '2019-05-30 23:10:21'],\n",
       "              dtype='datetime64[ns]', name='created_at', length=179496, freq=None)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5.分析api调用次数情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.794960e+05</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.877739e+06</td>\n",
       "      <td>7.175909</td>\n",
       "      <td>1393.177832</td>\n",
       "      <td>108.419626</td>\n",
       "      <td>359.880374</td>\n",
       "      <td>187.812208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.012494e+06</td>\n",
       "      <td>4.325160</td>\n",
       "      <td>1499.486073</td>\n",
       "      <td>79.640693</td>\n",
       "      <td>638.919827</td>\n",
       "      <td>224.464813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.626440e+05</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>36.550000</td>\n",
       "      <td>3.210000</td>\n",
       "      <td>36.550000</td>\n",
       "      <td>36.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.825233e+06</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>607.707500</td>\n",
       "      <td>83.410000</td>\n",
       "      <td>198.280000</td>\n",
       "      <td>144.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.811510e+06</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1154.905000</td>\n",
       "      <td>97.120000</td>\n",
       "      <td>256.090000</td>\n",
       "      <td>167.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.981455e+06</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1834.117500</td>\n",
       "      <td>116.990000</td>\n",
       "      <td>374.410000</td>\n",
       "      <td>202.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.019163e+09</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>142650.550000</td>\n",
       "      <td>18896.640000</td>\n",
       "      <td>142468.270000</td>\n",
       "      <td>71325.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id          count   res_time_sum   res_time_min  \\\n",
       "count  1.794960e+05  179496.000000  179496.000000  179496.000000   \n",
       "mean   6.877739e+06       7.175909    1393.177832     108.419626   \n",
       "std    6.012494e+06       4.325160    1499.486073      79.640693   \n",
       "min    1.626440e+05       1.000000      36.550000       3.210000   \n",
       "25%    3.825233e+06       4.000000     607.707500      83.410000   \n",
       "50%    6.811510e+06       7.000000    1154.905000      97.120000   \n",
       "75%    9.981455e+06      10.000000    1834.117500     116.990000   \n",
       "max    2.019163e+09      31.000000  142650.550000   18896.640000   \n",
       "\n",
       "        res_time_max   res_time_avg  \n",
       "count  179496.000000  179496.000000  \n",
       "mean      359.880374     187.812208  \n",
       "std       638.919827     224.464813  \n",
       "min        36.550000      36.000000  \n",
       "25%       198.280000     144.000000  \n",
       "50%       256.090000     167.000000  \n",
       "75%       374.410000     202.000000  \n",
       "max    142468.270000   71325.000000  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='created_at'>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['2019-5-1']['count'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-05-01 00:00:00</th>\n",
       "      <td>4.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 01:00:00</th>\n",
       "      <td>2.272727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 02:00:00</th>\n",
       "      <td>1.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 03:00:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 04:00:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 05:00:00</th>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 06:00:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 07:00:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 08:00:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 09:00:00</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 10:00:00</th>\n",
       "      <td>1.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 11:00:00</th>\n",
       "      <td>1.604651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 12:00:00</th>\n",
       "      <td>3.298246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 13:00:00</th>\n",
       "      <td>6.866667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 14:00:00</th>\n",
       "      <td>10.483333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 15:00:00</th>\n",
       "      <td>12.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 16:00:00</th>\n",
       "      <td>9.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 17:00:00</th>\n",
       "      <td>7.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 18:00:00</th>\n",
       "      <td>6.783333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 19:00:00</th>\n",
       "      <td>9.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 20:00:00</th>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 21:00:00</th>\n",
       "      <td>10.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 22:00:00</th>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 23:00:00</th>\n",
       "      <td>5.083333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         count\n",
       "created_at                    \n",
       "2019-05-01 00:00:00   4.428571\n",
       "2019-05-01 01:00:00   2.272727\n",
       "2019-05-01 02:00:00   1.833333\n",
       "2019-05-01 03:00:00        NaN\n",
       "2019-05-01 04:00:00        NaN\n",
       "2019-05-01 05:00:00   2.000000\n",
       "2019-05-01 06:00:00        NaN\n",
       "2019-05-01 07:00:00        NaN\n",
       "2019-05-01 08:00:00        NaN\n",
       "2019-05-01 09:00:00   1.000000\n",
       "2019-05-01 10:00:00   1.400000\n",
       "2019-05-01 11:00:00   1.604651\n",
       "2019-05-01 12:00:00   3.298246\n",
       "2019-05-01 13:00:00   6.866667\n",
       "2019-05-01 14:00:00  10.483333\n",
       "2019-05-01 15:00:00  12.333333\n",
       "2019-05-01 16:00:00   9.916667\n",
       "2019-05-01 17:00:00   7.666667\n",
       "2019-05-01 18:00:00   6.783333\n",
       "2019-05-01 19:00:00   9.850000\n",
       "2019-05-01 20:00:00  11.000000\n",
       "2019-05-01 21:00:00  10.416667\n",
       "2019-05-01 22:00:00   8.000000\n",
       "2019-05-01 23:00:00   5.083333"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2=df['2019-5-1']\n",
    "df2=df2[['count']].resample('1H').mean()\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='created_at'>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2['count'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 凌晨两点到十一点api访问少  下午两三点，晚上八九点访问多"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7.分析一天中api响应时间"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['2019-5-1'][['res_time_sum','res_time_min','res_time_max','res_time_avg']].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data=df['2019-5-1'].resample('20T').mean()\n",
    "data\n",
    "data[['res_time_sum','res_time_min','res_time_max','res_time_avg']].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 在业务高峰时间段，最大响应时间和平均 响应时间都有所上升"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8. 分析连续的几天数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='created_at'>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "df['2019-5-1':'2019-5-3']['count'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='created_at'>"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "df['2019-5-1':'2019-5-5']['count'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "#每天的业务高峰时段都比较相似"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9. 分析周末访问量是否有增加"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "            ...\n",
       "            5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n",
       "           dtype='int64', name='created_at', length=895)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['2019-5-11'].index.weekday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>created_at</th>\n",
       "      <th>weekday</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:00:07</th>\n",
       "      <td>2019162542</td>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:01:07</th>\n",
       "      <td>162644</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:02:07</th>\n",
       "      <td>162742</td>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:03:07</th>\n",
       "      <td>162808</td>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:04:07</th>\n",
       "      <td>162943</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             id  count  res_time_sum  res_time_min  \\\n",
       "created_at                                                           \n",
       "2018-11-01 00:00:07  2019162542      8       1057.31         88.75   \n",
       "2018-11-01 00:01:07      162644      5        749.12        103.79   \n",
       "2018-11-01 00:02:07      162742      5        845.84        136.31   \n",
       "2018-11-01 00:03:07      162808      9       1305.52         90.12   \n",
       "2018-11-01 00:04:07      162943      3        568.89        138.45   \n",
       "\n",
       "                     res_time_max  res_time_avg           created_at  weekday  \n",
       "created_at                                                                     \n",
       "2018-11-01 00:00:07        177.72         132.0  2018-11-01 00:00:07        3  \n",
       "2018-11-01 00:01:07        240.38         149.0  2018-11-01 00:01:07        3  \n",
       "2018-11-01 00:02:07        225.73         169.0  2018-11-01 00:02:07        3  \n",
       "2018-11-01 00:03:07        196.61         145.0  2018-11-01 00:03:07        3  \n",
       "2018-11-01 00:04:07        232.02         189.0  2018-11-01 00:04:07        3  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['weekday']=df.index.weekday\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>created_at</th>\n",
       "      <th>weekday</th>\n",
       "      <th>weekend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:00:07</th>\n",
       "      <td>2019162542</td>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:01:07</th>\n",
       "      <td>162644</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:02:07</th>\n",
       "      <td>162742</td>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:03:07</th>\n",
       "      <td>162808</td>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:04:07</th>\n",
       "      <td>162943</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             id  count  res_time_sum  res_time_min  \\\n",
       "created_at                                                           \n",
       "2018-11-01 00:00:07  2019162542      8       1057.31         88.75   \n",
       "2018-11-01 00:01:07      162644      5        749.12        103.79   \n",
       "2018-11-01 00:02:07      162742      5        845.84        136.31   \n",
       "2018-11-01 00:03:07      162808      9       1305.52         90.12   \n",
       "2018-11-01 00:04:07      162943      3        568.89        138.45   \n",
       "\n",
       "                     res_time_max  res_time_avg           created_at  weekday  \\\n",
       "created_at                                                                      \n",
       "2018-11-01 00:00:07        177.72         132.0  2018-11-01 00:00:07        3   \n",
       "2018-11-01 00:01:07        240.38         149.0  2018-11-01 00:01:07        3   \n",
       "2018-11-01 00:02:07        225.73         169.0  2018-11-01 00:02:07        3   \n",
       "2018-11-01 00:03:07        196.61         145.0  2018-11-01 00:03:07        3   \n",
       "2018-11-01 00:04:07        232.02         189.0  2018-11-01 00:04:07        3   \n",
       "\n",
       "                     weekend  \n",
       "created_at                    \n",
       "2018-11-01 00:00:07    False  \n",
       "2018-11-01 00:01:07    False  \n",
       "2018-11-01 00:02:07    False  \n",
       "2018-11-01 00:03:07    False  \n",
       "2018-11-01 00:04:07    False  "
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['weekend']=df['weekday'].isin({5,6})\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "weekend  created_at\n",
       "False    0              3.239120\n",
       "         1              1.668388\n",
       "         2              1.162551\n",
       "         3              1.086705\n",
       "         4              1.155556\n",
       "         5              1.136364\n",
       "         6              1.000000\n",
       "         7              1.000000\n",
       "         8              1.000000\n",
       "         9              1.080000\n",
       "         10             1.239011\n",
       "         11             2.031690\n",
       "         12             4.195845\n",
       "         13             6.668042\n",
       "         14             8.260503\n",
       "         15             8.934448\n",
       "         16             8.466504\n",
       "         17             6.784996\n",
       "         18             6.717731\n",
       "         19             8.655913\n",
       "         20            10.536496\n",
       "         21            10.846906\n",
       "         22             9.034164\n",
       "         23             5.946834\n",
       "True     0              3.467782\n",
       "         1              1.741849\n",
       "         2              1.161826\n",
       "         3              1.050000\n",
       "         4              1.076923\n",
       "         5              1.333333\n",
       "         6              1.000000\n",
       "         7              1.000000\n",
       "         8              1.071429\n",
       "         9              1.144928\n",
       "         10             1.254111\n",
       "         11             1.992958\n",
       "         12             4.031889\n",
       "         13             6.905772\n",
       "         14             8.851321\n",
       "         15             9.858422\n",
       "         16             9.420550\n",
       "         17             7.334743\n",
       "         18             7.342150\n",
       "         19             9.270430\n",
       "         20            11.173609\n",
       "         21            11.695043\n",
       "         22            10.419916\n",
       "         23             7.025452\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['weekend',df.index.hour])['count'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='created_at'>"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(['weekend',df.index.hour])['count'].mean().unstack(level=0).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "##周末的下午和晚上，比非周末访问量多一些"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
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